Cosmos3 Env Troubleshoot

Cosmos3 Environment Troubleshooting

When to use this skill

• Use when a user hits an error during installation, environment setup, or first run
• Use when a traceback mentions torch, CUDA, missing modules, or shared libraries
• Use when Docker or container setup fails
• Use when checkpoint downloads fail or HuggingFace auth errors appear

Path convention

All paths below are relative to this file's location (.agents/skills/cosmos3-env-troubleshoot/).

Step 1: Match against known errors

Check the error message against the table below. Each row links to the canonical fix in the docs.

| Error signature | Cause | Fix location |

| ----------------------------------------------------------------------------- | ------------------------------------ | -------------------------------------------------------------------------------------------------------- |

| ImportError: cannot import name '_functionalization' from 'torch._C' | NGC container library conflict | ../../../docs/setup.md § PyTorch Import Issue — run export LD_LIBRARY_PATH='' |

| ModuleNotFoundError: No module named 'cosmos_framework' | Package not installed | ../../../docs/setup.md § Dependency Issue — run uv sync --all-extras --group=cu130-train --reinstall |

| ModuleNotFoundError: No module named <other> | Dependency missing | ../../../docs/setup.md § Dependency Issue — reinstall venv |

| fatal error: Python.h: No such file or directory | Broken Python / uv install | ../../../docs/setup.md § Python Issue — reinstall uv + venv from scratch |

| OSError: <lib>: cannot open shared object file | CUDA version mismatch | ../../../docs/setup.md § CUDA Issue — install matching cuda-toolkit-<major> |

| docker: Error response from daemon: unknown or invalid runtime name: nvidia | Docker nvidia runtime not configured | ../../../docs/setup.md § Docker Container — run sudo nvidia-ctk runtime configure --runtime=docker |

| HuggingFace 401 / download failures | Auth or license not accepted | ../../../docs/setup.md § Downloading Base Checkpoints — check HF_TOKEN, accept license agreement |

Step 2: If no documented fix matches, try common remediation

Run these diagnostic commands to collect information, then attempt fixes in order:

Diagnostic commands

# System
uname -a
cat /etc/os-release | head -5

# Python
python --version
which python

# CUDA
nvidia-smi
python -c "import torch; print(f'torch={torch.__version__}, cuda={torch.version.cuda}')"

# Package
uv pip list | head -20

Remediation ladder (try in order)

• Clear library path: export LD_LIBRARY_PATH=''
• Reinstall venv: uv sync --all-extras --group=cu130-train --reinstall (or cu128-train on older drivers; drop -train only if you intentionally want the inference-only group)
• Reinstall uv + venv from scratch:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   uv python install --reinstall
   rm -rf .venv
   uv sync --all-extras --group=cu130-train --reinstall
   source .venv/bin/activate

• Check CUDA version alignment: the major CUDA version from nvidia-smi must match torch.version.cuda
• Try Docker: if the host environment is too broken, fall back to the Docker container (see ../../../docs/setup.md)

Step 3: If still unresolved, generate a bug report

If none of the above resolves the issue, collect environment information and present the user with a pre-filled bug report they can submit as a GitHub issue.

Fill in the template below by running the diagnostic commands and inserting the results:

## Environment

- **OS**: <output of `uname -a`>
- **Python**: <output of `python --version`>
- **CUDA (system)**: <output of `nvidia-smi` — first line with driver/CUDA version>
- **CUDA (torch)**: <output of `python -c "import torch; print(torch.version.cuda)">`>
- **torch version**: <output of `python -c "import torch; print(torch.__version__)">`>
- **cosmos_framework version**: <output of `python -c "import cosmos_framework; print(cosmos_framework.__version__)"` or "not installed">
- **Installation method**: <uv sync / uv pip / Docker / NGC container>

## Error

<full traceback>

## What was tried

1. <list each remediation step attempted and its result>

## Additional context

<any other relevant details — multi-GPU setup, custom CUDA install, etc.>